IMAGE

Fig. 2

ID: ZDB-IMAGE-221120-17
Source: Figures for Cho et al., 2021

Image

Figure Caption

Fig. 2

Fig. 2. Neural networks for sparse decomposition and deconvolution. (a) Architecture of BEAR. The network takes the raw video

Y

$Y$

and produces the low rank component

L

$L$

. The non-negative sparse component

S

$S$

is obtained as

S = ReLU (Y - L)

$S = \textit {ReLU }(Y-L)$

. (b) Deconvolution network architecture. The deconvolved images are obtained by feeding the wide-field images to the network.

Acknowledgments

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